Secure Local LLM Assistant Using Encrypted Vector Retrieval
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Solution Overview
Problem
The pervasive dependence on cloud computing environments for large language models (LLMs) raises significant privacy and security concerns, as sensitive user data is harvested and analyzed, leading to data breaches and unauthorized uses of personal data, particularly in light of the need for effective and effective solutions.
Innovation Solution
A method for chatting in a secured computing environment includes providing a firewall that restricts the LLM's operation behind a firewall to ensure no sensitive data is transmitted/sent outside of the local environment, utilizing an encrypted vector database for content retrieval and response generation, and applying artificial intelligence techniques to user data for task anticipation and action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud computing environments are used for LLM processing, then language processing capabilities are improved, but user data privacy and security deteriorate
Solution Approach 1:
The patent introduces an encrypted vector database as an intermediary layer between the user's local environment and the LLM processing system. This database stores embeddings of user content in encrypted form, allowing the LLM to access and process user data without directly exposing the raw sensitive information, thus maintaining privacy while enabling sophisticated language processing capabilities.
Solution Approach 2:
The system segments the LLM processing into multiple components: local encrypted vector database storage, secure embedding generation, and controlled LLM access. By dividing the processing pipeline into separate security zones with controlled data flow, the system enables cloud-based LLM capabilities while minimizing privacy risks through architectural segmentation.
2Power
If data is transmitted to external processors for LLM analysis, then processing power is improved, but data security deteriorates
Solution Approach 1:
Instead of transmitting original sensitive user data to external LLM processors, the system creates and transmits encrypted vector embeddings as copies. These embeddings capture the semantic meaning of user content without exposing the actual sensitive information, allowing powerful external processing while maintaining security through the use of data copies rather than originals.
Solution Approach 2:
The system transforms user data from its original form into vector embeddings with different parameters (numerical representations of semantic meaning). This parameter transformation allows the data to be processed by powerful external LLMs while the transformed nature of the data prevents reconstruction of sensitive original information, thus enabling high processing power without compromising security.
3Object-affected harmful factors
If local LLM processing is implemented, then data privacy is improved, but computational resource requirements worsen
Solution Approach 1:
The patent extracts the most computationally intensive components (full LLM processing) to external cloud services while keeping only the essential local components (encrypted vector database, embedding generation). This extraction allows the system to maintain strong local privacy protection while avoiding the burden of running complete LLMs locally, thus reducing device complexity requirements.
Solution Approach 2:
Instead of implementing complete LLM processing locally, the system performs only the necessary local actions (storing encrypted embeddings, generating queries) and leaves the excessive computational burden to external services. This partial local implementation achieves sufficient privacy protection without requiring full local computational capabilities.
Data Source
AI summary
A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.